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复旦大学附属肿瘤医院泌尿外科,复旦大学上海医学院肿瘤学系,上海 200032
Received:07 July 2021,
Revised:2021-09-23,
Published:30 January 2022
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Wenhao XU, Xi TIAN, Aihetaimujiang·, et al. A systematic review of current advancements of artificial intelligence in genitourinary cancers[J]. China Oncology, 2022, 32(1): 68-74.
Wenhao XU, Xi TIAN, Aihetaimujiang·, et al. A systematic review of current advancements of artificial intelligence in genitourinary cancers[J]. China Oncology, 2022, 32(1): 68-74. DOI: 10.19401/j.cnki.1007-3639.2022.01.009.
近年来
机器学习和神经网络技术的进步使得人工智能(artificial intelligence
AI)在指导临床诊断、治疗和资源投入等方面产生了巨大影响。在泌尿系统肿瘤领域
AI在改善前列腺癌、肾癌和膀胱癌的诊断和治疗方面取得了诸多进步
已可利用机器学习和神经网络技术自动化进行预后预测、治疗计划优化和患者随访教育等。有证据表明
AI指导可以显著降低泌尿系统肿瘤的诊断和治疗管理的主观性。尽管AI在泌尿系统肿瘤中的应用已经成为现代科技的热点
但对比真实世界的医疗决策时
AI仍然存在明显的局限性。通过对AI目前的优势和不足进行概述
旨在为未来AI在泌尿系统肿瘤的精准化、个性化诊治和长期管理中的应用提供参考。
Recently
advances in machine learning and neural network technology have allowed artificial intelligence (AI) to further promote guidance of clinical diagnosis
treatment and resource expenditures. In genitourinary cancers
AI has made huge progress in improving the diagnosis and treatment of prostate
kidney and bladder cancers. Numerous studies have developed methods to utilize neural networks to automate prognostic prediction
treatment plan optimization and patient follow-up education. Obviously
AI guidance could markedly reduce the subjectivity of diagnosis and treatment management of genitourinary cancers. However
although the application of AI in cancer treatment has become a research hotspot in modern technology
there still exist obvious limitations of AI management when compared with real-world clinical strategies. Therefore
this article summarized the current advantages and disadvantages of AI to provide novel insights for the future application of AI in the precision
personalized diagnosis and treatment
and long-term management of both patients and urologists.
YOU W P , HENNEBERG M . Cancer incidence increasing globally: the role of relaxed natural selection [J ] . Evol Appl , 2018 , 11 ( 2 ): 140 - 152 . DOI: 10.1111/eva.2018.11.issue-2 http://doi.org/10.1111/eva.2018.11.issue-2 http://doi.wiley.com/10.1111/eva.2018.11.issue-2 http://doi.wiley.com/10.1111/eva.2018.11.issue-2
TROGDON J G , FALCHOOK A D , BASAK R , et al. Total medicare costs associated with diagnosis and treatment of prostate cancer in elderly men [J ] . JAMA Oncol , 2019 , 5 ( 1 ): 60 - 66 . DOI: 10.1001/jamaoncol.2018.3701 http://doi.org/10.1001/jamaoncol.2018.3701 http://oncology.jamanetwork.com/article.aspx?doi=10.1001/jamaoncol.2018.3701 http://oncology.jamanetwork.com/article.aspx?doi=10.1001/jamaoncol.2018.3701
ADIR O , POLEY M , CHEN G , et al. Integrating artificial intelligence and nanotechnology for precision cancer medicine [J ] . Adv Mater , 2020 , 32 ( 13 ): e1901989 .
GALMARINI C M , LUCIUS M . Artificial intelligence: a disruptive tool for a smarter medicine [J ] . Eur Rev Med Pharmacol Sci , 2020 , 24 ( 13 ): 7462 - 7474 .
HAMAMOTO R , SUVARNA K , YAMADA M , et al. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine [J ] . Cancers (Basel) , 2020 , 12 ( 12 ): 3532 - 3564 . DOI: 10.3390/cancers12123532 http://doi.org/10.3390/cancers12123532 https://www.mdpi.com/2072-6694/12/12/3532 https://www.mdpi.com/2072-6694/12/12/3532
HUNSBERGER J , SIMON C , ZYLBERBERG C , et al. Improving patient outcomes with regenerative medicine: how the regenerative medicine manufacturing society plans to move the needle forward in cell manufacturing, standards, 3D bioprinting, artificial intelligence-enabled automation, education, and training [J ] . Stem Cells Transl Med , 9 ( 7 ): 728 - 733 . DOI: 10.1002/sctm.19-0389 http://doi.org/10.1002/sctm.19-0389 https://academic.oup.com/stcltm/article/9/7/728-733/6406862 https://academic.oup.com/stcltm/article/9/7/728-733/6406862
IQBAL U , CELI L A , LI Y J . How can artificial intelligence make medicine more preemptive ?[J ] . J Med Internet Res , 2020 , 22 ( 8 ): e17211 . DOI: 10.2196/17211 http://doi.org/10.2196/17211 https://www.jmir.org/2020/8/e17211 https://www.jmir.org/2020/8/e17211
KAR A , SUBASH A , RAO V U S . Reactive artificial intelligence using big data in the era of precision medicine [J ] . JAMA Surg , 2020 , 155 ( 7 ): 671 .
KAUL V , ENSLIN S , GROSS S A . History of artificial intelligence in medicine [J ] . Gastrointest Endosc , 2020 , 92 ( 4 ): 807 - 812 . DOI: 10.1016/j.gie.2020.06.040 http://doi.org/10.1016/j.gie.2020.06.040 https://linkinghub.elsevier.com/retrieve/pii/S0016510720344667 https://linkinghub.elsevier.com/retrieve/pii/S0016510720344667
LANGLOTZ C P , ALLEN B , ERICKSON B J , et al. A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/the academy workshop [J ] . Radiology , 2019, 291 ( 3 ): 781 - 791 . DOI: 10.1148/radiol.2019190613 http://doi.org/10.1148/radiol.2019190613 http://pubs.rsna.org/doi/10.1148/radiol.2019190613 http://pubs.rsna.org/doi/10.1148/radiol.2019190613
GOLDENBERG S L , NIR G , SALCUDEAN S E . A new era: artificial intelligence and machine learning in prostate cancer [J ] . Nat Rev Urol , 2019 , 16 ( 7 ): 391 - 403 . DOI: 10.1038/s41585-019-0193-3 http://doi.org/10.1038/s41585-019-0193-3 https://doi.org/10.1038/s41585-019-0193-3 https://doi.org/10.1038/s41585-019-0193-3
OLCZAK J , FAHLBERG N , MAKI A , et al. Artificial intelligence for analyzing orthopedic trauma radiographs [J ] . Acta Orthop , 2017 , 88 ( 6 ): 581 - 586 . DOI: 10.1080/17453674.2017.1344459 http://doi.org/10.1080/17453674.2017.1344459 https://www.tandfonline.com/doi/full/10.1080/17453674.2017.1344459 https://www.tandfonline.com/doi/full/10.1080/17453674.2017.1344459
BI W L , HOSNY A , SCHABATH M B , et al. Artificial intelligence in cancer imaging: clinical challenges and applications [J ] . CA Cancer J Clin , 2019 , 69 ( 2 ): 127 - 157 .
CHEN P H C , GADEPALLI K , MACDONALD R , et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis [J ] . Nat Med , 2019 , 25 ( 9 ): 1453 - 1457 . DOI: 10.1038/s41591-019-0539-7 http://doi.org/10.1038/s41591-019-0539-7 https://doi.org/10.1038/s41591-019-0539-7 https://doi.org/10.1038/s41591-019-0539-7
BAGHERI M H , AHLMAN M A , LINDENBERG L , et al. Advances in medical imaging for the diagnosis and management of common genitourinary cancers [J ] . Urol Oncol , 2017 , 35 ( 7 ): 473 - 491 . DOI: 10.1016/j.urolonc.2017.04.014 http://doi.org/10.1016/j.urolonc.2017.04.014 https://linkinghub.elsevier.com/retrieve/pii/S1078143917301862 https://linkinghub.elsevier.com/retrieve/pii/S1078143917301862
HEMAL A K , MENON M . Robotics in urology [J ] . Curr Opin Urol , 2004 , 14 ( 2 ): 89 - 93 . DOI: 10.1097/00042307-200403000-00007 http://doi.org/10.1097/00042307-200403000-00007 http://journals.lww.com/00042307-200403000-00007 http://journals.lww.com/00042307-200403000-00007
MAMDANI M , SLUTSKY A S . Artificial intelligence in intensive care medicine [J ] . Intensive Care Med , 2021 , 47 ( 2 ): 147 - 149 . DOI: 10.1007/s00134-020-06203-2 http://doi.org/10.1007/s00134-020-06203-2 https://doi.org/10.1007/s00134-020-06203-2 https://doi.org/10.1007/s00134-020-06203-2
FRANZMEIER N , KOUTSOULERIS N , BENZINGER T , et al. Predicting sporadic Alzheimer’s disease progression via inherited Alzheimer’s disease-informed machine-learning [J ] . Alzheimers Dement , 2020 , 16 ( 3 ): 501 - 511 . DOI: 10.1002/alz.v16.3 http://doi.org/10.1002/alz.v16.3 https://onlinelibrary.wiley.com/toc/15525279/16/3 https://onlinelibrary.wiley.com/toc/15525279/16/3
GOULD M K , HUANG B Z , TAMMEMAGI M C , et al. Machine learning for early lung cancer identification using routine clinical and laboratory data [J ] . Am J Respir Crit Care Med , 2021 , 204 ( 4 ): 445 - 453 . DOI: 10.1164/rccm.202007-2791OC http://doi.org/10.1164/rccm.202007-2791OC https://www.atsjournals.org/doi/10.1164/rccm.202007-2791OC https://www.atsjournals.org/doi/10.1164/rccm.202007-2791OC
AUFFENBERG G B , GHANI K R , RAMANI S , et al. AskMUSIC: leveraging a clinical registry to develop a new machine learning model to inform patients of prostate cancer treatments chosen by similar men [J ] . Eur Urol , 2019 , 75 ( 6 ): 901 - 907 . DOI: 10.1016/j.eururo.2018.09.050 http://doi.org/10.1016/j.eururo.2018.09.050 https://linkinghub.elsevier.com/retrieve/pii/S0302283818307401 https://linkinghub.elsevier.com/retrieve/pii/S0302283818307401
LEE C I , HOUSSAMI N , ELMORE J G , et al. Pathways to breast cancer screening artificial intelligence algorithm validation [J ] . Breast , 2020 , 52 : 146 - 149 . DOI: 10.1016/j.breast.2019.09.005 http://doi.org/10.1016/j.breast.2019.09.005 https://linkinghub.elsevier.com/retrieve/pii/S0960977619305545 https://linkinghub.elsevier.com/retrieve/pii/S0960977619305545
POORTMANS P M P , TAKANEN S , MARTA G N , et al. Winter is over: the use of artificial intelligence to individualise radiation therapy for breast cancer [J ] . Breast , 2020 , 49 : 194 - 200 . DOI: 10.1016/j.breast.2019.11.011 http://doi.org/10.1016/j.breast.2019.11.011 https://linkinghub.elsevier.com/retrieve/pii/S0960977619311038 https://linkinghub.elsevier.com/retrieve/pii/S0960977619311038
BIBAULT J E , GIRAUD P , BURGUN A . Big Data and machine learning in radiation oncology: state of the art and future prospects [J ] . Cancer Lett , 2016 , 382 ( 1 ): 110 - 117 . DOI: 10.1016/j.canlet.2016.05.033 http://doi.org/10.1016/j.canlet.2016.05.033 https://linkinghub.elsevier.com/retrieve/pii/S0304383516303469 https://linkinghub.elsevier.com/retrieve/pii/S0304383516303469
LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 . DOI: 10.1038/nature14539 http://doi.org/10.1038/nature14539 https://doi.org/10.1038/nature14539 https://doi.org/10.1038/nature14539
REEL P S , REEL S , PEARSON E , et al. Using machine learning approaches for multi-omics data analysis: a review [J ] . Biotechnol Adv , 2021 , 49 : 107739 - 107763 . DOI: 10.1016/j.biotechadv.2021.107739 http://doi.org/10.1016/j.biotechadv.2021.107739 https://linkinghub.elsevier.com/retrieve/pii/S0734975021000458 https://linkinghub.elsevier.com/retrieve/pii/S0734975021000458
SIMON G , DINARDO C D , TAKAHASHI K , et al. Applying artificial intelligence to address the knowledge gaps in cancer care [J ] . Oncologist , 2019 , 24 ( 6 ): 772 - 782 . DOI: 10.1634/theoncologist.2018-0257 http://doi.org/10.1634/theoncologist.2018-0257 https://academic.oup.com/oncolo/article/24/6/772/6439311 https://academic.oup.com/oncolo/article/24/6/772/6439311
DE SILVA D , RANASINGHE W , BANDARAGODA T , et al. Machine learning to support social media empowered patients in cancer care and cancer treatment decisions [J ] . PLoS One , 2018 , 13 ( 10 ): e0205855 . DOI: 10.1371/journal.pone.0205855 http://doi.org/10.1371/journal.pone.0205855 https://dx.plos.org/10.1371/journal.pone.0205855 https://dx.plos.org/10.1371/journal.pone.0205855
NISSAN N , ALLWEIS T , MENES T , et al. Breast MRI during lactation: effects on tumor conspicuity using dynamic contrast-enhanced (DCE) in comparison with diffusion tensor imaging (DTI) parametric maps [J ] . Eur Radiol , 2020 , 30 ( 2 ): 767 - 777 . DOI: 10.1007/s00330-019-06435-x http://doi.org/10.1007/s00330-019-06435-x https://doi.org/10.1007/s00330-019-06435-x https://doi.org/10.1007/s00330-019-06435-x
LI C , WANG S , YAN J L , et al. Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging [J ] . J Neurosurg , 2019 , 132 ( 5 ): 1465 - 1472 . DOI: 10.3171/2018.12.JNS182926 http://doi.org/10.3171/2018.12.JNS182926 https://thejns.org/view/journals/j-neurosurg/132/5/article-p1465.xml https://thejns.org/view/journals/j-neurosurg/132/5/article-p1465.xml
PAPAGNO C , MATTAVELLI G , CASAROTTI A , et al. Defective recognition and naming of famous people from voice in patients with unilateral temporal lobe tumours [J ] . Neuropsychologia , 2018 , 116(Pt B) : 194 - 204 .
SHAO F , HUANG Q , WANG C , et al. Artificial neural networking model for the prediction of early occlusion of bilateral plastic stent placement for inoperable hilar cholangiocarcinoma [J ] . Surg Laparosc Endosc Percutan Tech , 2018 , 28 ( 2 ): e54-e58 .
REHER R , KIM H W , ZHANG C , et al. A convolutional neural network-based approach for the rapid annotation of molecularly diverse natural products [J ] . J Am Chem Soc , 2020 , 142 ( 9 ): 4114 - 4120 . DOI: 10.1021/jacs.9b13786 http://doi.org/10.1021/jacs.9b13786 https://pubs.acs.org/doi/10.1021/jacs.9b13786 https://pubs.acs.org/doi/10.1021/jacs.9b13786
ALBERTSEN P C . Patient decision-making: where are we going ?[J ] . Eur Urol , 2019 , 75 ( 6 ): 908 - 909 . DOI: 10.1016/j.eururo.2018.10.024 http://doi.org/10.1016/j.eururo.2018.10.024 https://linkinghub.elsevier.com/retrieve/pii/S0302283818308121 https://linkinghub.elsevier.com/retrieve/pii/S0302283818308121
ARVANITI E , FRICKER K S , MORET M , et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning [J ] . Sci Rep , 2018 , 13 ( 1 ): 12054 - 12064 .
LUCAS M , JANSEN I , SAVCI-HEIJINK C D , et al. Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies [J ] . Virchows Arch , 2019 , 475 ( 1 ): 77 - 83 . DOI: 10.1007/s00428-019-02577-x http://doi.org/10.1007/s00428-019-02577-x https://doi.org/10.1007/s00428-019-02577-x https://doi.org/10.1007/s00428-019-02577-x
FEHR D , VEERARAGHAVAN H , WIBMER A , et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images [J ] . Proc Natl Acad Sci USA , 2015 , 112 ( 46 ): E6265-E6273 .
NAYAN M , SALARI K , BOZZO A , et al. Predicting survival after radical prostatectomy: variation of machine learning performance by race [J ] . Prostate , 2021 , 81 ( 16 ): 1355 - 1364 . DOI: 10.1002/pros.v81.16 http://doi.org/10.1002/pros.v81.16 https://onlinelibrary.wiley.com/toc/10970045/81/16 https://onlinelibrary.wiley.com/toc/10970045/81/16
ZHU Y , MO M , WEI Y , et al. Epidemiology and genomics of prostate cancer in Asian men [J ] . Nat Rev Urol , 2021 , 18 ( 5 ): 282 - 301 . DOI: 10.1038/s41585-021-00442-8 http://doi.org/10.1038/s41585-021-00442-8 https://doi.org/10.1038/s41585-021-00442-8 https://doi.org/10.1038/s41585-021-00442-8
叶定伟 . 守正创新 , 笃行致远: 中国前列腺癌诊治历程回顾与展望 [J ] . 中华泌尿外科杂志 , 2020 , 41 ( 11 ): 801 - 806 .
YE D W . Keeping integrity and innovation, striving for the future: retrospect and prospect of diagnosis and treatment of prostate cancer in China [J ] . Chin J Urol , 2020 ( 11 ): 801 - 806 .
ROSSI S H , KLATTE T , USHER-SMITH J , et al. Epidemiology and screening for renal cancer [J ] . World J Urol , 2018 , 36 ( 9 ): 1341 - 1353 . DOI: 10.1007/s00345-018-2286-7 http://doi.org/10.1007/s00345-018-2286-7 https://doi.org/10.1007/s00345-018-2286-7 https://doi.org/10.1007/s00345-018-2286-7
HAN S , HWANG S I , LEE H J . The classification of renal cancer in 3-phase CT images using a deep learning method [J ] . J Digit Imaging , 2019 , 32 ( 4 ): 638 - 643 . DOI: 10.1007/s10278-019-00230-2 http://doi.org/10.1007/s10278-019-00230-2 https://doi.org/10.1007/s10278-019-00230-2 https://doi.org/10.1007/s10278-019-00230-2
HOLDBROOK D A , HUBER R G , MARZINEK J K , et al. Multiscale modeling of innate immune receptors: endotoxin recognition and regulation by host defense peptides [J ] . Pharmacol Res , 2019 , 147 : 104372 - 104377 . DOI: 10.1016/j.phrs.2019.104372 http://doi.org/10.1016/j.phrs.2019.104372 https://linkinghub.elsevier.com/retrieve/pii/S1043661819305742 https://linkinghub.elsevier.com/retrieve/pii/S1043661819305742
DREICER R . Tyrosine kinase inhibitors compared with cytokine therapy for metastatic renal cell carcinoma: overview of recent clinical trials differentiating clinical response and adverse effects [J ] . Clin Genitourin Cancer , 2006 , 5 ( Suppl 1 ): S19-S23 .
UHLIG J , LEHA A , DELONGE L M , et al. Radiomic features and machine learning for the discrimination of renal tumor histological subtypes: a pragmatic study using clinical-routine computed tomography [J ] . Cancers (Basel) , 2020 , 12 ( 10 ): 2010 - 2012 . DOI: 10.3390/cancers12082010 http://doi.org/10.3390/cancers12082010 https://www.mdpi.com/2072-6694/12/8/2010 https://www.mdpi.com/2072-6694/12/8/2010
BUCHNER A , KENDLBACHER M , NUHN P , et al. Outcome assessment of patients with metastatic renal cell carcinoma under systemic therapy using artificial neural networks [J ] . Clin Genitourin Cancer , 2012 , 10 ( 1 ): 37 - 42 . DOI: 10.1016/j.clgc.2011.10.001 http://doi.org/10.1016/j.clgc.2011.10.001 https://linkinghub.elsevier.com/retrieve/pii/S1558767311000656 https://linkinghub.elsevier.com/retrieve/pii/S1558767311000656
MA C G , XU W H , XU Y , et al. Identification and validation of novel metastasis-related signatures of clear cell renal cell carcinoma using gene expression databases [J ] . Am J Transl Res , 2020 , 12 ( 8 ): 4108 - 4126 .
XU W H , XU Y , WANG J , et al. Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment [J ] . Aging (Albany NY) , 2019 , 11 ( 17 ): 6999 - 7020 .
BRAUN D A , HOU Y , BAKOUNY Z , et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma [J ] . Nat Med , 2020 , 26 ( 6 ): 909 - 918 . DOI: 10.1038/s41591-020-0839-y http://doi.org/10.1038/s41591-020-0839-y https://doi.org/10.1038/s41591-020-0839-y https://doi.org/10.1038/s41591-020-0839-y
SMITH C C , CHAI S , WASHINGTON A R , et al. Machine-learning prediction of tumor antigen immunogenicity in the selection of therapeutic epitopes [J ] . Cancer Immunol Res , 2019 , 7 ( 10 ): 1591 - 1604 . DOI: 10.1158/2326-6066.CIR-19-0155 http://doi.org/10.1158/2326-6066.CIR-19-0155 http://cancerimmunolres.aacrjournals.org/lookup/doi/10.1158/2326-6066.CIR-19-0155 http://cancerimmunolres.aacrjournals.org/lookup/doi/10.1158/2326-6066.CIR-19-0155
JIANG P , GU S Q , PAN D , et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response [J ] . Nat Med , 2018 , 24 ( 10 ): 1550 - 1558 . DOI: 10.1038/s41591-018-0136-1 http://doi.org/10.1038/s41591-018-0136-1 https://doi.org/10.1038/s41591-018-0136-1 https://doi.org/10.1038/s41591-018-0136-1
XU W H , XU Y , TIAN X , et al. Large-scale transcriptome profiles reveal robust 20-signatures metabolic prediction models and novel role of G6PC in clear cell renal cell carcinoma [J ] . J Cell Mol Med , 2020 , 24 ( 16 ): 9012 - 9027 . DOI: 10.1111/jcmm.v24.16 http://doi.org/10.1111/jcmm.v24.16 https://onlinelibrary.wiley.com/toc/15824934/24/16 https://onlinelibrary.wiley.com/toc/15824934/24/16
XU W , TIAN X , LIU W , et al. m6A regulator-mediated methylation modification model predicts prognosis, tumor microenvironment characterizations and response to immunotherapies of clear cell renal cell carcinoma [J ] . Front Oncol , 2021 , 11 : 709579 - 709590 . DOI: 10.3389/fonc.2021.709579 http://doi.org/10.3389/fonc.2021.709579 https://www.frontiersin.org/articles/10.3389/fonc.2021.709579/full https://www.frontiersin.org/articles/10.3389/fonc.2021.709579/full
MALATS N , REAL F X . Epidemiology of bladder cancer [J ] . Hematol Clin N Am , 2015 , 29 ( 2 ): 177 - 189 . DOI: 10.1016/j.hoc.2014.10.001 http://doi.org/10.1016/j.hoc.2014.10.001 https://linkinghub.elsevier.com/retrieve/pii/S0889858814001427 https://linkinghub.elsevier.com/retrieve/pii/S0889858814001427
EMINAGA O , EMINAGA N , SEMJONOW A , et al. Diagnostic classification of cystoscopic images using deep convolutional neural networks [J ] . JCO Clin Cancer Inform , 2018 , 2 : 1 - 8 .
GARAPATI S S , HADJIISKI L , CHA K H , et al. Urinary bladder cancer staging in CT urography using machine learning [J ] . Med Phys , 2017 , 44 ( 11 ): 5814 - 5823 . DOI: 10.1002/mp.2017.44.issue-11 http://doi.org/10.1002/mp.2017.44.issue-11 http://doi.wiley.com/10.1002/mp.2017.44.issue-11 http://doi.wiley.com/10.1002/mp.2017.44.issue-11
CHA K H , HADJIISKI L , CHAN H P , et al. Bladder cancer treatment response assessment in CT using radiomics with deep-learning [J ] . Sci Rep , 2017 , 7 ( 1 ): 1 - 12 . DOI: 10.1038/s41598-016-0028-x http://doi.org/10.1038/s41598-016-0028-x http://www.nature.com/articles/s41598-016-0028-x http://www.nature.com/articles/s41598-016-0028-x
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